Original link:http://tecdat.cn/?p=23104 

Polynomial regression is a nonlinear relationship between independent X variables and causal Y variables. Fit this type of regression is essential when we analyze volatility data with some curvature. In this article, we’ll learn how to use polynomial regression data to fit a curve and plot it in Python. We use the NumPy and Matplotlib libraries throughout this tutorial.

We’ll start by loading the modules required for this tutorial.

import numpy as np
import matplotlib.pyplot as plt

We need test data, which we can generate as shown in the figure below. You can also use your own data set.

Train_x = np.array(x) train_y = np.array(y)

We will visually examine the X data by creating a scatter plot.

plt.scatter(train\_x, train\_y)

 

Next, we will use the PolymonialFeatures class to define the polynomial model to fit on the training data.

fit\_transform(train\_x.reshape(-1, 1))

We need a linear model, which we will define and fit on the training data. And then we use this model to predict the X data.

liniearModel.fit(xpol, train_y\[:,\])

Finally, we will plot the fitting curve.

plt.plot(train_x, polyfit, color = 'red')

In this article, we have briefly looked at how to fit polynomial regression data in Python.

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